Assessing Migratory Performance in Collision-Surviving Birds With Motus¶
This notebook analyzes migratory bird data from the Motus Wildlife Tracking System, specifically focusing on the Motus Northeast Collision Project. The project tracked six species that survived window collisions in the Lake Erie region (Cleveland and Pittsburgh) during 2018:
- American Woodcock
- White-throated Sparrow
- Gray Catbird
- Ovenbird
- Wood Thrush
- Magnolia Warbler
Background¶
Window collisions pose a severe threat to migratory bird populations, estimated to kill up to one billion birds annually. This research examines birds that survived collisions and were rehabilitated before being released with Motus nanotags for tracking. To learn more about the bird windows collision phenomenon, check out this article.
Research Focus¶
The analysis assesses migratory performance of collision-surviving birds by examining:
- Detection probabilities
- Migration patterns
- Travel speeds and distances
- Comparative analysis between rehabilitated and wild birds
Data Source¶
Data comes from the Motus Wildlife Tracking System (specifically the Northeast Collision Project), which uses automated radio-telemetry technology to track tagged birds with high geospatial accuracy.
The complete research paper can be found here.
Analysis Questions¶
Population Demographics¶
- What is the distribution of rehab vs non-rehab birds?
- Total counts by status
- Breakdown by species
- Detection frequencies
- Repeat detections analysis (birds detected multiple times)
Spatial Analysis¶
- What are the movement patterns?
- Geographic distribution of detections
- Range/territory size analysis
- Movement corridors identification
Initial import of Python libraries
Helper Functions¶
Main Analysis¶
Successfully loaded with mac_roman encoding Detections dataframe loaded with 17989 rows and 17 columns CPU times: user 88.1 ms, sys: 98.9 ms, total: 187 ms Wall time: 5.4 s
With the dataframe loaded, we can begin our analysis.
1. Population Demographics¶
- What is the distribution of rehab vs non-rehab birds?
- Total counts by status
- Breakdown by species
- Detection frequencies
- Repeat detections analysis (birds detected multiple times)
Found 36 unique tags Found 6 unique species: 1. White-throated Sparrow 2. American Woodcock 3. Gray Catbird 4. Ovenbird 5. Wood Thrush 6. Magnolia Warbler
Detections per species: speciesEN.x Wood Thrush 9374 Gray Catbird 6394 American Woodcock 1415 Magnolia Warbler 719 Ovenbird 53 White-throated Sparrow 13 Name: count, dtype: int64 Top 5 receivers by detection count: recvDeployName PARC Banding 9877 PARC Honey Hut 7967 Churchill - Fen 15 Long_Point_Tip 12 43.6:-79.35 11 Name: count, dtype: int64 Bottom 5 receivers by detection count: recvDeployName Napanee LOSH 1 PARC Mt. Laurel 1 Curries 1 Merlin 1 39.8:-79.7 1 Name: count, dtype: int64
Summary Statistics: ----------------- AMWO: 5 total birds (4 rehab [80.0%], 1 wild) WTSP: 5 total birds (1 rehab [20.0%], 4 wild) GRCA: 9 total birds (6 rehab [66.7%], 3 wild) OVEN: 7 total birds (4 rehab [57.1%], 3 wild) WOTH: 6 total birds (3 rehab [50.0%], 3 wild)
<module 'matplotlib.pyplot' from '/Users/km7yh/miniconda3/envs/gis_dev/lib/python3.11/site-packages/matplotlib/pyplot.py'>
<Figure size 1500x600 with 0 Axes>
Detection Patterns Key Observations:¶
- Species Detection Peaks: The highest peak is seen for the Ovenbird (Rehab) in early June, reaching nearly 1,000 detections. The Ovenbird (Wild) also shows a high detection rate around the same time but at a lower intensity.
- Species with Lower Detections: Other species like the American Woodcock, White-throated Sparrow, Gray Catbird, Magnolia Warbler, and Wood Thrush have relatively lower and more sporadic detection patterns throughout the year. Their peaks are considerably lower than the Ovenbird detections.
- Seasonal Patterns: There appears to be a seasonal trend where bird detections, especially for Ovenbird species, spike in early summer and decrease significantly afterward, with some smaller fluctuations seen in the fall.
Spatial Analysis¶
Using the MovingPandas library, we can analyze the movement patterns of the birds by creating trajectory data structures.
Built on top of GeoPandas and Pandas, this library provides a robust framework for trajectory analysis using the time-series data we already have from Motus.
According to its documentation, MovingPandas follows the trajectories = timeseries with geometries approach of modeling movement data.
Ultimately the trajectory represents a series of connected points in space and time. Adding them to the dataframe as a GeoDataFrame with a geometry column makes them easier to work with compared to the base data alone.
Trajectory Type: <class 'movingpandas.trajectory_collection.TrajectoryCollection'>
| tag_id | species | rehab_status | duration_hours | length_km | avg_speed_kmh | |
|---|---|---|---|---|---|---|
| 0 | 24345 | Ovenbird | Y | 86.592500 | 1946.312371 | 22.476685 |
| 1 | 24376 | Gray Catbird | N | 0.284722 | 10.167210 | 35.709227 |
| 2 | 28413 | Magnolia Warbler | N | 97.042778 | 524.176398 | 5.401498 |
| 3 | 28414 | White-throated Sparrow | N | 2.795000 | 246.577979 | 88.221101 |
| 4 | 28415 | Magnolia Warbler | Y | 344.381389 | 0.000000 | 0.000000 |
| 5 | 28416 | White-throated Sparrow | N | 0.001389 | 0.000000 | 0.000000 |
| 6 | 28419 | Magnolia Warbler | Y | 0.216944 | 0.000000 | 0.000000 |
| 7 | 28420 | American Woodcock | Y | 141.028889 | 0.000000 | 0.000000 |
| 8 | 28422 | Gray Catbird | Y | 20.001389 | 0.000000 | 0.000000 |
| 9 | 28423 | White-throated Sparrow | Y | 3.750833 | 178.202314 | 47.510059 |
| 10 | 28425 | Ovenbird | Y | 6.085556 | 212.006862 | 34.837717 |
| 11 | 28426 | White-throated Sparrow | N | 1.318056 | 63.250001 | 47.987356 |
| 12 | 28428 | American Woodcock | Y | 170.930278 | 0.000000 | 0.000000 |
| 13 | 28429 | Gray Catbird | Y | 17.289167 | 0.000000 | 0.000000 |
| 14 | 28433 | Magnolia Warbler | Y | 20.295000 | 233.226498 | 11.491821 |
| 15 | 28434 | Wood Thrush | N | 3037.487778 | 0.000000 | 0.000000 |
| 16 | 28436 | Gray Catbird | Y | 678.899444 | 536.068231 | 0.789614 |
| 17 | 28440 | Wood Thrush | N | 1.468889 | 0.000000 | 0.000000 |
| 18 | 28441 | Gray Catbird | Y | 1085.320833 | 1080.091990 | 0.995182 |
| 19 | 28445 | American Woodcock | N | 3686.137778 | 54.662266 | 0.014829 |
| 20 | 28446 | Gray Catbird | Y | 35.117500 | 0.000000 | 0.000000 |
| 21 | 28448 | Wood Thrush | Y | 194.942500 | 0.000000 | 0.000000 |
| 22 | 28449 | American Woodcock | Y | 274.779167 | 0.000000 | 0.000000 |
| 23 | 28451 | American Woodcock | Y | 4058.609444 | 615.327421 | 0.151610 |
| 24 | 28452 | Ovenbird | N | 407.132778 | 1050.644321 | 2.580594 |
| 25 | 28453 | Gray Catbird | Y | 226.470278 | 0.000000 | 0.000000 |
| 26 | 28455 | Wood Thrush | Y | 78.456944 | 318.946884 | 4.065247 |
| 27 | 28457 | Wood Thrush | Y | 3344.478333 | 603.590712 | 0.180474 |
| 28 | 28458 | Gray Catbird | N | 0.912778 | 45.675547 | 50.040161 |
| 29 | 28460 | Gray Catbird | N | 74.215833 | 63.250001 | 0.852244 |
| 30 | 28761 | Ovenbird | N | 0.068056 | 0.000000 | 0.000000 |
| 31 | 28763 | Wood Thrush | N | 2877.911389 | 0.000000 | 0.000000 |
Overall Summary by Rehab Status:
duration_hours length_km \
count mean std min max count mean
rehab_status
N 13 783.60 1393.28 0.00 3686.14 13 158.34
Y 19 567.77 1143.10 0.22 4058.61 19 301.25
avg_speed_kmh
std min max count mean std min max
rehab_status
N 306.41 0.0 1050.64 13 17.75 28.58 0.0 88.22
Y 499.64 0.0 1946.31 19 6.45 13.61 0.0 47.51
Trajectory Counts by Species:
Wild Rehab Total
species
American Woodcock 1 4 5
Gray Catbird 3 6 9
Magnolia Warbler 1 3 4
Ovenbird 2 2 4
White-throated Sparrow 3 1 4
Wood Thrush 3 3 6
Detailed Species Summary:
duration_hours \
count mean std min
species rehab_status
American Woodcock N 1 3686.14 NaN 3686.14
Y 4 1161.34 1932.37 141.03
Gray Catbird N 3 25.14 42.50 0.28
Y 6 343.85 443.32 17.29
Magnolia Warbler N 1 97.04 NaN 97.04
Y 3 121.63 193.17 0.22
Ovenbird N 2 203.60 287.84 0.07
Y 2 46.34 56.93 6.09
White-throated Sparrow N 3 1.37 1.40 0.00
Y 1 3.75 NaN 3.75
Wood Thrush N 3 1972.29 1708.64 1.47
Y 3 1205.96 1852.93 78.46
length_km \
max count mean std
species rehab_status
American Woodcock N 3686.14 1 54.66 NaN
Y 4058.61 4 153.83 307.66
Gray Catbird N 74.22 3 39.70 27.04
Y 1085.32 6 269.36 451.36
Magnolia Warbler N 97.04 1 524.18 NaN
Y 344.38 3 77.74 134.65
Ovenbird N 407.13 2 525.32 742.92
Y 86.59 2 1079.16 1226.34
White-throated Sparrow N 2.80 3 103.28 128.07
Y 3.75 1 178.20 NaN
Wood Thrush N 3037.49 3 0.00 0.00
Y 3344.48 3 307.51 301.96
avg_speed_kmh \
min max count mean
species rehab_status
American Woodcock N 54.66 54.66 1 0.01
Y 0.00 615.33 4 0.04
Gray Catbird N 10.17 63.25 3 28.87
Y 0.00 1080.09 6 0.30
Magnolia Warbler N 524.18 524.18 1 5.40
Y 0.00 233.23 3 3.83
Ovenbird N 0.00 1050.64 2 1.29
Y 212.01 1946.31 2 28.66
White-throated Sparrow N 0.00 246.58 3 45.40
Y 178.20 178.20 1 47.51
Wood Thrush N 0.00 0.00 3 0.00
Y 0.00 603.59 3 1.42
std min max
species rehab_status
American Woodcock N NaN 0.01 0.01
Y 0.08 0.00 0.15
Gray Catbird N 25.30 0.85 50.04
Y 0.47 0.00 1.00
Magnolia Warbler N NaN 5.40 5.40
Y 6.63 0.00 11.49
Ovenbird N 1.82 0.00 2.58
Y 8.74 22.48 34.84
White-throated Sparrow N 44.17 0.00 88.22
Y NaN 47.51 47.51
Wood Thrush N 0.00 0.00 0.00
Y 2.30 0.00 4.07
Geographic distribution of detections¶
The Motus detection dataset originates from the Motus Northeast Collaboration network. Plotting the receiver locations and the detected bird movements on a map can help us visualize the coverage and detectability of the network.
The northeast region of the US is covered by 37 receivers (as of Q4 2019), as shown in the maps below.
Number of receivers: 37
Receivers are color-coded by the number of detections they have recorded in total.
Plotting Trajectories¶
Simple trajectories can be plotted with Matplotlib using with each bird's tag ID, using the MovingPandas TrajectoryCollection object. From the base dataset this dataset is just a series of points with a location and timestamp, so this plot gives a quick representation of the detected bird movements as lines as well. MovingPandas has a number of additional functionalities for smoothing or otherwise interpolating the trajectories (my custom function is not one of them).
Plotting the detected bird movements on a map can help us visualize the coverage and detectability of the network.
From the Folium map below, we can see that the birds were detected in a number of locations across the northeast region of the US and southern Ontario on both sides of Lake Erie and Lake Ontario.
White-throated Sparrow
Image sources: Wikimedia Commons, Animalia.bio
American Woodcock
Image sources: Wikimedia Commons, Animalia.bio
Gray Catbird
Image sources: Wikimedia Commons, Animalia.bio
Ovenbird
Image sources: Wikimedia Commons, Animalia.bio
Wood Thrush
Image sources: Wikimedia Commons, Animalia.bio
Magnolia Warbler
Image sources: Wikimedia Commons, Animalia.bio
Challenges¶
While conducting this analysis, several challenges were encountered that may impact the interpretation of results:
Limited Sample Size
- The dataset included a relatively small number of birds, which poses significant limitations for drawing broad conclusions.
- Species Diversity: The limited number of species represented in the sample may not accurately reflect the overall diversity of the bird population in the study area. This could lead to an underestimation of the ecological richness and the potential impacts of environmental changes on various species.
- Unique Birds Detected: The small sample size also means that the number of unique individuals detected was low. This raises concerns about the representativeness of the data and may highlight striking declines in bird populations that are already occurring.
- For instance, if only a few individuals of a species are detected, it may indicate that the population is at risk or declining, which could be a critical signal for conservation efforts.
Implications for Conservation
- The findings from this limited dataset should be interpreted with caution. While they may suggest trends or patterns, they may not provide a complete picture of the bird populations in the area.
- Future studies should aim to increase sample sizes and include a broader range of species to better understand population dynamics and inform effective conservation strategies.
Addressing these challenges is crucial for ensuring that the insights gained from this analysis contribute meaningfully to our understanding of avian ecology and the urgent need for conservation efforts in the face of declining bird populations.
Future Research Opportunities¶
While this current workbook focuses on Population Demographics and Spatial Analysis, there are several other valuable analyses that could be conducted with this ecological data. Below are some suggested topics for future exploration:
Detection Network Analysis
Receiver Effectiveness: Assess the performance of the receiver network in detecting birds.
- Coverage Analysis: Map the geographical distribution of receivers and identify gaps in coverage.
- Detection Frequency: Analyze the number of detections per receiver to determine which receivers are most effective.
- Temporal Patterns: Investigate how detection rates vary over time, identifying peak detection periods and potential seasonal trends.
Comparative Analysis
Behavioral Comparisons: Examine differences in movement and behavior between rehab and wild birds.
- Movement Patterns: Compare trajectory characteristics, such as speed and distance traveled, to understand how rehabilitation affects bird behavior.
- Survival and Detection Rates: Analyze the survival rates of rehab birds versus wild birds, including how often they are detected over time.
- Territory Size: Calculate and compare home ranges of rehab and wild birds to assess habitat use and territorial behavior.
- Seasonal Behavior Patterns: Explore how movement and detection patterns change with the seasons, providing insights into migratory behavior and habitat preferences.
These analyses can provide deeper insights into the ecological dynamics of bird populations, the effectiveness of rehabilitation efforts, and the role of environmental factors in shaping bird behavior. Future work could expand on these themes to enhance our understanding of avian ecology and inform conservation strategies.